In meta-analysis of intervention studies,
sensitivity analysis methods based on selection models
provide more objective and stable ways
to address publication bias issues than
the widely used graphical methods,
such as
the funnel plot and the trim-and-fill method
([1]}, [2]}),
and then should play important roles in practice.
Recently,
sensitivity analysis methods have been extended to more complicated situations
including network meta-analysis
([3]})
and diagnostic studies
([4]}, [5]}, [6]}).
The importance of the sensitivity analysis approach should be more emphasized
in meta-analysis of diagnostic test accuracy on SROC,
since the graphical methods
used in meta-analysis of intervention studies
are not reasonably applicable
due to the bivariate nature of the problem.
Sensitivity analysis in
multivariate or network meta-analyses have been proposed
based on the extensions of
Heckman-type
([7]}, [8]}, [9]})
selection functions.
In this study,
we took a different approach by
extending the likelihood based sensitivity analysis by [10]},
which enabled us to use a selection function
dependent on the study-specific cutoff values.
This feature is appealing in practice
since in many diagnostic studies,
main results are often reported with study-specific cutoff values so that
the selective publication process should be influenced by them.
